Machine Learning-based Research Articles

Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery.

Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.

Named Entity Recognition in Travel-Related Search Queries

This paper addresses the problem of named entity recognition (NER) in travel-related search queries. NER is an important step toward a richer understanding of user-generated inputs in information retrieval systems. NER in queries is challenging due to minimal context and few structural clues. NER in restricted-domain queries is useful in vertical search applications, for example following query classification in general search. This paper describes an efficient machine learningbased solution for the high-quality extraction of semantic entities from query inputs in a restricted-domain information retrieval setting. We apply a conditional random field (CRF) sequence model to travel-domain search queries and achieve high-accuracy results. Our approach yields an overall F1 score of 86.4% on a heldout test set, outperforming a baseline score of 82.0% on a CRF with standard features. The resulting NER classifier is currently in use in a real-life travel search engine.

Online System for Grid Resource Monitoring and Machine Learning-Based Prediction

Resource allocation and job scheduling are the core functions of grid computing. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building a distributed system for grid resource monitoring and prediction. In this paper, we present the design and evaluation of a system architecture for grid resource monitoring and prediction. We discuss the key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models. Evaluations are performed on a prototype system. Our experimental results indicate that the efficiency and accuracy of our system meet the demand of online system for grid resource monitoring and prediction.